R Notebook

#setwd("C:/Users/erussek/forage_jsp/analysis")
setwd("/Users/evanrussek/forage_jsp/analysis")
rm(list = ls())
library(tidyr)

Attaching package: ‘tidyr’

The following object is masked from ‘package:Matrix’:

    expand

library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

library(zoo)

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

library(ggplot2)
library(ggpubr)

Loading required package: magrittr

Attaching package: ‘magrittr’

The following object is masked from ‘package:tidyr’:

    extract

library(RcppRoll)
library(knitr)

data <- read.csv('data/run4_data.csv')
head(data)

data <- data %>% ungroup() %>% bind_cols(s_num = group_indices(., subjectID))
data <- data %>% mutate(subj = factor(s_num))

travel_keys = data %>% ungroup() %>% select(travel_key) %>% unique()
clean_subj_data <- function(subj_data, travel_keys){
  travel_key_hard = travel_keys$travel_key[1]
  travel_key_easy = travel_keys$travel_key[2]
  
  subj_data <- data %>%select(round, phase, reward_obs, reward_true, lag, exit, start_reward, n_travel_steps,
                                    travel_key, subjectID, trial_num, s_num, correct_key) %>% group_by(trial_num) %>%
    mutate(press_num = 1:n(), round = as.factor(round), 
           rep_number = case_when(trial_num < 7 ~ 1, TRUE ~ 2)) %>%
    mutate(phase = replace(phase, phase == "Harvest", "HARVEST"))

  subj_data <- subj_data %>% group_by(trial_num) %>% 
    mutate(travel_key = replace(travel_key, travel_key == "", first(travel_key[travel_key != ""]))) %>%
    mutate(travel_key_cond = case_when(travel_key == travel_key_hard   ~ "HARD",
                                       travel_key == travel_key_easy  ~ "EASY")) %>%
    filter(!is.na(travel_key_cond))
}

# clean all the data and bind
datalist <- list()
for (i in 1:10){
  subj_data <- data %>% filter(s_num == i)
  subj_data <- clean_subj_data(subj_data, travel_keys)
  subj_data <- ungroup(subj_data)
  datalist[[i]] <- subj_data
}

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#cdata <- do.call(rbind,datalist)
cdata <- bind_rows(datalist)

# make a function to plot reward on each game... 
plot_subj_reward_v_press <- function(subj_data){
  
  s_num <- subj_data$s_num[1]
  
  # reward plots which show the threshold... 
  rew_plot <- ggplot(subj_data, aes(x = press_num, y = reward_obs, group = round))  +
    #geom_rect(data = t1_data, aes(xmin = press_num - 0.5, xmax = press_num + 0.5, ymin = -Inf, ymax = Inf, fill = round)) +
    geom_point(aes(color = phase)) + facet_grid(n_travel_steps ~ travel_key_cond) + theme(legend.position = "none") + ylim(0,110) + ggtitle(paste("subj: ", s_num))
  
 # plot_an <- annotate_figure(plot, top = paste('subj: ', s_num))
  plot(rew_plot)
                             
}
for (s in 1:22){
  subj_data <- cdata %>% filter(s_num == s)
  plot_subj_reward_v_press(subj_data)  
}

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get_trial_exits <- function(thdata){
    # get harvest
  last_phase <- last(thdata$phase)
  
  # go through data.. if last phase was harvest, remove that round... 
  if (last_phase == "HARVEST"){
    last_round <- last(thdata$round)
    # get the last reward ops...
    last_reward_ob <- last(thdata$reward_obs[!is.na(thdata$reward_obs)])
    
    #if (last_reward_ob > 8){
    thdata <- thdata %>% filter(round != last_round)
    #}
  }
  
  # now select harvest data
  thdata <- thdata %>% filter(phase == "HARVEST", !is.na(reward_obs)) # find out why reward_true has so many .na
  
  ## get either last true reward observed
  return_tbl <- thdata %>% 
                  group_by(round) %>% 
                      summarise(s_num = first(s_num), last_reward = last(reward_obs),
                          trial_num = first(trial_num),
                          n_travel_steps = first(n_travel_steps),
                          travel_key_cond = first(travel_key_cond),
                          rep_number = first(rep_number)) %>% ungroup()
  return(return_tbl)
}

#sdata %>% group_by(trial_num) %>%
#  do(get_trial_exits(.))

exit_data <- cdata %>% group_by(s_num, trial_num) %>%
  do(get_trial_exits(.)) %>% ungroup() %>% mutate(subj = as.factor(s_num))

library(ggpubr)
exit_data <- exit_data %>% mutate(round_num = as.integer(as.character(round)))
for (s in 1:22){
  p <- ggplot(exit_data %>% filter(s_num == s), aes(x = round_num, y = last_reward)) + geom_point() + geom_line() +
  facet_grid(n_travel_steps ~ travel_key_cond) + theme_minimal()  + ylim(0,100) + ylab('last reward collected') + xlab('harvest number')
  
  #plot(p)
  #ggarrange(p)
  a <- annotate_figure(p, right = "# travel steps", top = "travel step difficulty", fig.lab = paste('subj: ' , s))
  plot(a)
}

geom_path: Each group consists of only one observation. Do you need to adjust the
group aesthetic?

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# do some aggregating over exit thresholds... 
# just get the mean for each subject for each timepoint, collapse accross rep number...
mn_exit <- exit_data %>% group_by(s_num, n_travel_steps, travel_key_cond) %>%
  summarise(rep_exit_thresh = mean(last_reward), trial_num = mean(trial_num)) %>% 
  group_by(s_num, n_travel_steps, travel_key_cond) %>%
  summarise(exit_thresh = mean(rep_exit_thresh), trial_num = mean(trial_num)) %>%
  mutate(subj = as.factor(s_num)) %>% ungroup()

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# now just plot this for each subject -- draw lines to show easy - hard effect
ggplot(mn_exit, aes(x = travel_key_cond, y = exit_thresh)) + geom_dotplot(binaxis = 'y', aes(fill = subj), dotsize = 1.2) + geom_line(aes(group = subj, color = subj), size = 1.2) +
  facet_grid(.~n_travel_steps) + theme_minimal() + labs(y = 'Mean Last Reward Collected', x = 'Travel Sequence', subtitle = '# Travel Presses') #theme(legend.position = "none")

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ggplot(mn_exit, aes(x = factor(n_travel_steps), y = exit_thresh)) +
  geom_dotplot(binaxis = 'y', aes(fill = subj), dotsize = 1.2) + 
  geom_line(aes(group = subj, color = subj), size = 1.2) +
  facet_grid(.~travel_key_cond) + 
  theme_minimal() + 
  labs(y = 'Mean Last Reward Collected', x = 'Travel Key Condition', subtitle = '# Travel Presses') 

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  #theme(legend.position = "none")

# plot the means for each of these...
gmn_exit <- mn_exit %>% group_by(n_travel_steps, travel_key_cond) %>% summarise(gm_thresh = mean(exit_thresh), gsd_thresh = sd(exit_thresh)/sqrt(n()))

ggplot(gmn_exit, aes(x = factor(n_travel_steps), y = gm_thresh, color = travel_key_cond)) +
  geom_line(aes(group = travel_key_cond), size = 2) +
  geom_errorbar(aes(ymin = gm_thresh - gsd_thresh, ymax = gm_thresh+gsd_thresh), width = .1, size = 2) + 
geom_point(size = 4) + ylab('group mean exit threshold') + xlab('# travel steps') + labs(color = 'travel effort') + theme_minimal()

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library(optimx)
library(lmerTest)
exit_model <- lmer(last_reward ~ n_travel_steps + travel_key_cond  + (1  + n_travel_steps + travel_key_cond |subj), data = exit_data,  control = lmerControl(optimizer ="optimx",optCtrl=list(method='nlminb')))
summary(exit_model)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: last_reward ~ n_travel_steps + travel_key_cond + (1 + n_travel_steps +  
    travel_key_cond | subj)
   Data: exit_data
Control: lmerControl(optimizer = "optimx", optCtrl = list(method = "nlminb"))

REML criterion at convergence: 6956.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.1827 -0.5108  0.0002  0.5046  5.3563 

Random effects:
 Groups   Name                Variance  Std.Dev. Corr       
 subj     (Intercept)         290.72330 17.0506             
          n_travel_steps        0.06055  0.2461  -0.27      
          travel_key_condHARD  68.01256  8.2470  -0.25  0.26
 Residual                      87.82008  9.3712             
Number of obs: 928, groups:  subj, 22

Fixed effects:
                    Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)         54.95291    3.71001 20.77742  14.812 1.63e-12 ***
n_travel_steps      -0.24696    0.06318 19.11207  -3.909 0.000935 ***
travel_key_condHARD -5.06293    1.89212 17.91918  -2.676 0.015465 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) n_trv_
n_trvl_stps -0.309       
trvl_k_HARD -0.256  0.206

library(broom.mixed)

Registered S3 methods overwritten by 'broom.mixed':
  method         from 
  augment.lme    broom
  augment.merMod broom
  glance.lme     broom
  glance.merMod  broom
  glance.stanreg broom
  tidy.brmsfit   broom
  tidy.gamlss    broom
  tidy.lme       broom
  tidy.merMod    broom
  tidy.rjags     broom
  tidy.stanfit   broom
  tidy.stanreg   broom

td <- tidy(exit_model, conf.int = TRUE)
tdsome <- td %>% filter(term == 'n_travel_steps' | term == 'travel_key_condHARD')

tdsome <- tdsome %>% mutate(parameter = case_when(term == 'n_travel_steps' ~ 'travel step', term == 'travel_key_condHARD' ~ 'travel effort cond'))

ggplot(tdsome, aes(estimate,parameter,color = parameter)) + 
  geom_point(size = 2)  +
geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), size = 1.2) + theme(legend.position = "none") + theme_minimal() + ylab('fixed effect') + xlab('estimate (# points)') + ggtitle('linear mixed effects model - effect on exit threshold') + theme(axis.text.y = element_text(angle = 90, hjust = .5))

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# reaction times... 


## a bit more sensible... 
lag_data <- cdata %>% select(s_num, rep_number, travel_key_cond, n_travel_steps, phase, trial_num, lag, correct_key, round, trial_num) %>% mutate(log_lag = log(lag)) %>% group_by(s_num, trial_num, round, phase) %>% slice(-1) %>% ungroup() %>% mutate(subj = as.factor(s_num))

ggplot(lag_data, aes(x=lag)) + geom_histogram(binwidth = 10) + xlim(50,300) + facet_wrap( ~ s_num)

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#ggplot(lag_data, aes(x=log_lag))  + geom_histogram(binwidth = .01) + facet_wrap( ~ s_num)

filt_lag <- lag_data %>% group_by(s_num) %>% filter(log_lag < (mean(log_lag) + 3*sd(log_lag)) , log_lag > (mean(log_lag) - 3*sd(log_lag))) %>% ungroup()

ggplot(filt_lag, aes(x=lag)) + geom_histogram(binwidth = 10)  + facet_wrap(~s_num) + xlim(50,350)

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ggplot(filt_lag, aes(x=log_lag)) + geom_histogram(binwidth = .02) + facet_wrap(~s_num)

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NA
NA
NA

## with a run, plot the mean lag 
round_filt_lag <- filt_lag %>% 
  group_by(s_num, n_travel_steps, travel_key_cond, round, phase) %>%
  summarise(trial_num = first(trial_num), mean_lag = mean(lag), mean_log_lag = mean(log_lag)) %>% mutate(round_num = as.integer(as.character(round)))

# plot mean lag as a function of round number
lag_by_round <- round_filt_lag %>% group_by(s_num, round_num) %>% summarise(mean_lag = mean(mean_lag))

ggplot(lag_by_round, aes(x= round_num, y = mean_lag)) + geom_point() + geom_line() + facet_wrap(~s_num)

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trial_filt_lag <- round_filt_lag %>% 
  group_by(s_num, n_travel_steps, travel_key_cond, phase) %>%
  summarise(trial_num = first(trial_num), 
            mean_lag = mean(mean_lag), 
            mean_log_lag = mean(mean_log_lag)) %>% ungroup() %>% mutate(s_num = as.factor(s_num))

ggplot(trial_filt_lag, 
       aes(x = factor(n_travel_steps), y = mean_lag, fill = s_num)) + 
  geom_point(aes(color = s_num), size = 2)+ 
  geom_line(aes(group = s_num, color = s_num), size = 1.2) +
  facet_grid(phase ~ travel_key_cond) + theme_minimal()

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ggplot(trial_filt_lag, 
       aes(x = travel_key_cond, y = mean_lag, fill = s_num)) + 
  geom_point(aes(color = s_num), size = 2)+ 
  geom_line(aes(group = s_num, color = s_num), size = 1.2) +
  facet_grid(phase ~ n_travel_steps) + theme_minimal()

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rr

group_lag <- trial_filt_lag %>% group_by(n_travel_steps, travel_key_cond, phase) %>% summarise(gm_lag = mean(mean_lag), gml_lag = mean(mean_log_lag)) %>% ungroup()

ggplot(group_lag, aes(x = factor(n_travel_steps), y = gm_lag, color = travel_key_cond)) + geom_line(aes(group = travel_key_cond), size = 2) + facet_wrap(~phase) + geom_point(size = 2)

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rr # geom_errorbar(aes(ymin = gm_thresh - gsd_thresh, ymax = gm_thresh+gsd_thresh), width = .1, size = 2) + #geom_point(size = 4) + ylab(‘group mean exit threshold’) + xlab(‘# travel steps’) + labs(color = ‘travel effort’) + theme_minimal()

rr harvest_lag <- filt_lag %>% ungroup() %>% filter(phase == ) hl_model <- lmer(log_lag ~ trial_num + n_travel_steps + travel_key_cond + (1 + trial_num + n_travel_steps + travel_key_cond |subj), data = harvest_lag, control = lmerControl(optimizer =,optCtrl=list(method=‘nlminb’)))

rr summary(hl_model)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log_lag ~ trial_num + n_travel_steps + travel_key_cond + (1 +      trial_num + n_travel_steps + travel_key_cond | subj)
   Data: harvest_lag
Control: lmerControl(optimizer = \optimx\, optCtrl = list(method = \nlminb\))

REML criterion at convergence: -318425.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-8.1768 -0.4915 -0.0665  0.3443 12.1230 

Random effects:
 Groups   Name                Variance  Std.Dev. Corr             
 subj     (Intercept)         3.952e-02 0.198786                  
          trial_num           6.360e-04 0.025220 -0.62            
          n_travel_steps      2.484e-05 0.004984  0.13 -0.48      
          travel_key_condHARD 8.152e-03 0.090291 -0.01  0.16 -0.37
 Residual                     3.686e-02 0.191985                  
Number of obs: 689562, groups:  subj, 22

Fixed effects:
                      Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)          5.1600968  0.0423935 20.9468771 121.719   <2e-16 ***
trial_num           -0.0060976  0.0053850 21.1720321  -1.132    0.270    
n_travel_steps      -0.0005989  0.0010634 21.0256448  -0.563    0.579    
travel_key_condHARD  0.0150356  0.0192651 21.0227579   0.780    0.444    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) trl_nm n_trv_
trial_num   -0.620              
n_trvl_stps  0.130 -0.476       
trvl_k_HARD -0.007  0.159 -0.366
convergence code: 0
Model failed to converge with max|grad| = 0.00210383 (tol = 0.002, component 1)

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